Project: Finding Lane Lines on the Road¶
Import Packages¶
#importing some useful packages
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
import numpy as np
import cv2
%matplotlib inline
Read in an Image¶
#reading in an image
image = mpimg.imread('test_images/solidWhiteRight.jpg')
#printing out some stats and plotting
print('This image is:', type(image), 'with dimensions:', image.shape)
plt.imshow(image) # if you wanted to show a single color channel image called 'gray', for example, call as plt.imshow(gray, cmap='gray')
Ideas for Lane Detection Pipeline¶
Some OpenCV functions (beyond those introduced in the lesson) that might be useful for this project are:
cv2.inRange() for color selection
cv2.fillPoly() for regions selection
cv2.line() to draw lines on an image given endpoints
cv2.addWeighted() to coadd / overlay two images
cv2.cvtColor() to grayscale or change color
cv2.imwrite() to output images to file
cv2.bitwise_and() to apply a mask to an image
Check out the OpenCV documentation to learn about these and discover even more awesome functionality!
Helper Functions¶
Below are some helper functions to help get you started. They should look familiar from the lesson!
import math
def region_of_interest(img, vertices):
"""
Applies an image mask.
Only keeps the region of the image defined by the polygon
formed from `vertices`. The rest of the image is set to black.
"""
#defining a blank mask to start with
mask = np.zeros_like(img)
#defining a 3 channel or 1 channel color to fill the mask with depending on the input image
if len(img.shape) > 2:
channel_count = img.shape[2] # i.e. 3 or 4 depending on your image
ignore_mask_color = (255,) * channel_count
else:
ignore_mask_color = 255
#filling pixels inside the polygon defined by "vertices" with the fill color
cv2.fillPoly(mask, vertices, ignore_mask_color)
#returning the image only where mask pixels are nonzero
masked_image = cv2.bitwise_and(img, mask)
return masked_image
def grayscale(img):
"""Applies the Grayscale transform
This will return an image with only one color channel
but NOTE: to see the returned image as grayscale
(assuming your grayscaled image is called 'gray')
you should call plt.imshow(gray, cmap='gray')"""
return cv2.cvtColor(img, cv2.COLOR_RGB2GRAY)
# Or use BGR2GRAY if you read an image with cv2.imread()
# return cv2.cvtColor(img, cv2.COLOR_BGR2GRAY)
def gaussian_blur(img, kernel_size):
"""Applies a Gaussian Noise kernel"""
return cv2.GaussianBlur(img, (kernel_size, kernel_size), 0)
def canny(img, low_threshold, high_threshold):
"""Applies the Canny transform"""
return cv2.Canny(img, low_threshold, high_threshold)
def hough_lines(img, rho, theta, threshold, min_line_len, max_line_gap):
"""
`img` should be the output of a Canny transform.
Returns an image with hough lines drawn.
"""
lines = cv2.HoughLinesP(img, rho, theta, threshold, np.array([]), minLineLength=min_line_len, maxLineGap=max_line_gap)
line_img = np.zeros((img.shape[0], img.shape[1], 3), dtype=np.uint8)
draw_lines(line_img, lines)
return line_img
def drawLine(img, x, y, color=[255, 0, 0], thickness=5):
"""
Fit a line through x[], y[] and draw it on the image 'img'.
"""
if ((x is None) or ( y is None)):
return
if ((len(x) == 0) or (len(y) == 0)):
return
param = np.polyfit(x, y, 1)
m = param[0]
b = param[1]
Y_max = img.shape[0]
y1 = Y_max
x1 = int((y1 - b)/m)
y2 = int(Y_max/1.7)
x2 = int((y2 - b)/m)
cv2.line(img, (x1, y1), (x2, y2), color, thickness)
def draw_lines(img, lines, color=[255, 0, 0], thickness=5):
leftX = [] # Hold x-coordinates of left side of the lane
leftY = [] # Hold y-coordinates of left side of the lane
rightX = [] # Hold x-coordinates of right side of the lane
rightY = [] # Hold y-coordinates of right side of the lane
for line in lines:
for x1,y1,x2,y2 in line:
m = (y2 - y1)/(x2 - x1) # slope of the line segment
# Leaving the extreme line segments out for a smoother and better overlap
if ((m < -0.5) & (m > -0.8)):
leftX.append(x1)
leftX.append(x2)
leftY.append(y1)
leftY.append(y2)
elif((m > 0.5) & (m < 0.8)):
rightX.append(x1)
rightX.append(x2)
rightY.append(y1)
rightY.append(y2)
if( (len(leftX) > 0) & (len(leftY) > 0)):
drawLine(img, leftX, leftY, color, thickness)
if( (len(rightX) > 0) & (len(rightY) > 0)):
drawLine(img, rightX, rightY, color, thickness)
def weighted_img(img, initial_img, α=0.8, β=1., λ=0.):
"""
`img` is the output of the hough_lines(), An image with lines drawn on it.
Should be a blank image (all black) with lines drawn on it.
`initial_img` should be the image before any processing.
The result image is computed as follows:
initial_img * α + img * β + λ
NOTE: initial_img and img must be the same shape!
"""
return cv2.addWeighted(initial_img, α, img, β, λ)
def applyColorFilter(image):
"""
Filter the image to include only yellow and white pixels
"""
# Gets only white pixels
low_white = np.array([200, 200, 200])
high_white = np.array([255, 255, 255])
white_mask = cv2.inRange(image, low_white, high_white)
white_image = cv2.bitwise_and(image, image, mask=white_mask)
# Gets only yellow pixels
hsv = cv2.cvtColor(image, cv2.COLOR_BGR2HSV)
low_yellow = np.array([90,100,100])
high_yellow = np.array([110,255,255])
yellow_mask = cv2.inRange(hsv, low_yellow, high_yellow)
yellow_image = cv2.bitwise_and(image, image, mask=yellow_mask)
# Combining the images
final_img = cv2.addWeighted(white_image, 1., yellow_image, 1., 0.)
return final_img
Build a Lane Finding Pipeline¶
Build the pipeline and run your solution on all test_images. Make copies into the test_images_output directory, and you can use the images in your writeup report.
Try tuning the various parameters, especially the low and high Canny thresholds as well as the Hough lines parameters.
# TODO: Build your pipeline that will draw lane lines on the test_images
# then save them to the test_images directory.
# image ->region of interest -> gray -> gaussian_blur -> canny -> hough_lines -> draw_lines -> weighted_img
def pipeline(image):
xsize = image.shape[0]
ysize = image.shape[1]
max_width = image.shape[1]
max_height = image.shape[0]
# Region of interest
left_bottom = (0.05*max_width, max_height)
left_top = (0.4*max_width, max_height/1.8)
right_top = (max_width-0.4*max_width, max_height/1.8)
right_bottom = (max_width - 0.05*max_width, max_height)
vertices = np.array([ [left_bottom, left_top, right_top , right_bottom] ], np.int32)
# Gaussian Blur
kernel_size = 15
# Canny Edge Detector
low_threshold = 20
high_threshold = 100
# Hough Transform
rho = 1
theta = 1 * np.pi/180
threshold = 10 # minimum number of votes
min_line_len = 20 # minimum number of pixels making up a line
max_line_gap = 10
img = applyColorFilter(image)
gray = grayscale(img)
blur_gray = gaussian_blur(gray, kernel_size)
masked_image = region_of_interest(blur_gray, vertices)
edges = canny(masked_image, low_threshold, high_threshold)
#return edges
houghed = hough_lines(edges, rho, theta, threshold, min_line_length, max_line_gap)
#return houghed
# Draw lane lines on the original image
initial_image = image.astype('uint8')
annotated_image = weighted_img(houghed, initial_image)
# Display the image
return annotated_image
Test the Pipeline¶
plt.imshow(pipeline(image))
Test Images¶
Build your pipeline to work on the images in the directory "test_images"
You should make sure your pipeline works well on these images before you try the videos.
# Here are the test images
import os
import glob
image_name_list = glob.glob("./test_images/*.jpg")
fig = plt.figure(figsize=(40,40))
fig.subplots_adjust(wspace=0.08)
plt.clf()
for i, im in enumerate(image_name_list):
img = mpimg.imread(im)
ax = fig.add_subplot(3,2,i+1)
ax.imshow(img)
ax.axis('off')
# Testing the pipeline on test-images
import os
import glob
image_name_list = glob.glob("./test_images/*.jpg")
fig = plt.figure(figsize=(40,40))
fig.subplots_adjust(wspace=0.08)
plt.clf()
for i, im in enumerate(image_name_list):
img = mpimg.imread(im)
ax = fig.add_subplot(3,2,i+1)
ax.imshow(pipeline(img))
ax.axis('off')
Test on Videos¶
You know what's cooler than drawing lanes over images? Drawing lanes over video!
We can test our solution on two provided videos:
solidWhiteRight.mp4
solidYellowLeft.mp4
Note: if you get an import error when you run the next cell, try changing your kernel (select the Kernel menu above --> Change Kernel). Still have problems? Try relaunching Jupyter Notebook from the terminal prompt. Also, check out this forum post for more troubleshooting tips.
If you get an error that looks like this:
NeedDownloadError: Need ffmpeg exe.
You can download it by calling:
imageio.plugins.ffmpeg.download()Follow the instructions in the error message and check out this forum post for more troubleshooting tips across operating systems.
# Import everything needed to edit/save/watch video clips
from moviepy.editor import VideoFileClip
from IPython.display import HTML
def process_image(image):
# NOTE: The output you return should be a color image (3 channel) for processing video below
# TODO: put your pipeline here,
# you should return the final output (image where lines are drawn on lanes)
return pipeline(image)
Let's try the one with the solid white lane on the right first ...
white_output = 'solidWhiteRight_output.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip1 = VideoFileClip("test_videos/solidWhiteRight.mp4").subclip(0,5)
clip1 = VideoFileClip("./test_videos/solidWhiteRight.mp4")
white_clip = clip1.fl_image(process_image) #NOTE: this function expects color images!!
%time white_clip.write_videofile(white_output, audio=False)
Play the video inline, or if you prefer find the video in your filesystem (should be in the same directory) and play it in your video player of choice.
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(white_output))
Improve the draw_lines() function¶
At this point, if you were successful with making the pipeline and tuning parameters, you probably have the Hough line segments drawn onto the road, but what about identifying the full extent of the lane and marking it clearly as in the example video (P1_example.mp4)? Think about defining a line to run the full length of the visible lane based on the line segments you identified with the Hough Transform. As mentioned previously, try to average and/or extrapolate the line segments you've detected to map out the full extent of the lane lines. You can see an example of the result you're going for in the video "P1_example.mp4".
Go back and modify your draw_lines function accordingly and try re-running your pipeline. The new output should draw a single, solid line over the left lane line and a single, solid line over the right lane line. The lines should start from the bottom of the image and extend out to the top of the region of interest.
Now for the one with the solid yellow lane on the left. This one's more tricky!
yellow_output = 'solidYellowLeft.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
##clip2 = VideoFileClip('test_videos/solidYellowLeft.mp4').subclip(0,5)
clip2 = VideoFileClip('./test_videos/solidYellowLeft.mp4')
yellow_clip = clip2.fl_image(process_image)
%time yellow_clip.write_videofile(yellow_output, audio=False)
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(yellow_output))
Optional Challenge¶
Try your lane finding pipeline on the video below. Does it still work? Can you figure out a way to make it more robust? If you're up for the challenge, modify your pipeline so it works with this video and submit it along with the rest of your project!
challenge_output = 'challenge.mp4'
## To speed up the testing process you may want to try your pipeline on a shorter subclip of the video
## To do so add .subclip(start_second,end_second) to the end of the line below
## Where start_second and end_second are integer values representing the start and end of the subclip
## You may also uncomment the following line for a subclip of the first 5 seconds
clip3 = VideoFileClip('./test_videos/challenge.mp4').subclip(0,5)
#clip3 = VideoFileClip('./test_videos/challenge.mp4')
challenge_clip = clip3.fl_image(process_image)
%time challenge_clip.write_videofile(challenge_output, audio=False)
HTML("""
<video width="960" height="540" controls>
<source src="{0}">
</video>
""".format(challenge_output))